Causal Complex Climate Networks: Technicalities, Reconstruction from Data and Applications

Abstract

Complex networks have revolutionised the way non-linear dynamical (deterministic and stochastic) systems are represented and analysed. This paradigm shift owes itself to the ability to encode non-linear relationships in a hierarchical manner from the skeletal structure to deeper and subtle spatio-temporal dependencies. This talk aims to provide an overview of a class of complex networks known as causal networks that draw ideas from various fields including econometrics, social sciences, neuroscience, sciences, ecology and engineering. Of specific interest and relevance are the causal climate networks. The first half of the talk shall be devoted the overview and mathematical formalism of different types of (climate) causal networks with focus on Granger causal and convergent cross-mapping (CCM) class of networks, both of which are constructed from time-series data. The second part of this talk is devoted to a presentation of applications to reconstructing climate networks from data and ...